Vehicle routing and adaptive iterated local search within the hyflex hyper-heuristic framework

  • Authors:
  • James D. Walker;Gabriela Ochoa;Michel Gendreau;Edmund K. Burke

  • Affiliations:
  • School of Computer Science, University of Nottingham, UK;School of Computer Science, University of Nottingham, UK;CIRRELT, University of Montreal, Canada;Department of Computing Science and Mathematics, University of Stirling, UK

  • Venue:
  • LION'12 Proceedings of the 6th international conference on Learning and Intelligent Optimization
  • Year:
  • 2012

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Abstract

HyFlex (Hyper-heuristic Flexible framework) [15] is a software framework enabling the development of domain independent search heuristics (hyper-heuristics), and testing across multiple problem domains. This framework was used as a base for the first Cross-domain Heuristic Search Challenge, a research competition that attracted significant international attention. In this paper, we present one of the problems that was used as a hidden domain in the competition, namely, the capacitated vehicle routing problem with time windows. The domain implements a data structure and objective function for the vehicle routing problem, as well as many state-of- the-art low-level heuristics (search operators) of several types. The domain is tested using two adaptive variants of a multiple-neighborhood iterated local search algorithm that operate in a domain independent fashion, and therefore can be considered as hyper-heuristics. Our results confirm that adding adaptation mechanisms improve the performance of hyper-heuristics. It is our hope that this new and challenging problem domain can be used to promote research within hyper-heuristics, adaptive operator selection, adaptive multi-meme algorithms and autonomous control for search algorithms.